A new grey prediction model and its application to predicting landslide displacement

Wu, L. Z.; Li, S. H.; Huang, Rui; Xu, Qiang

doi:10.1016/j.asoc.2020.106543

Cited by 59 publications

(22 citation statements)

References 34 publications

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“…First, this study did not consider exogenous variables such as the price of magnesium and business indicators in the context of the demand for magnesium alloy. As multivariate models may yield better predictions than single-variable models (Hu, 2020 ; Ma et al, 2019 ; Wu et al, 2019 ), we will investigate the applicability of several models, such as the multivariable models of Moonchai and Chutsagulprom ( 2020 ), the cumulative sum operator in grey prediction using integral transformations of Wei et al ( 2020 ), and the background value optimization nonlinear model of Wu et al ( 2020 ), to forecast the demand for magnesium alloy. Next, since there are shortcomings of MAPE outlined by Vandeput (2021), we will investigate how to incorporate the other measures such as MAD and RMSE into the construction of the proposed models to improve forecasting accuracy.…”

Section: Discussionmentioning

confidence: 99%

Demand forecasting of green metal materials using non-equidistant grey prediction with robust nonlinear interval regression analysis

2021

Environ Dev Sustain

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The main advantages of magnesium alloys are that they are lightweight, easy to recycle, and have high vibration absorption. These unique characteristics make magnesium alloys important green metal materials for manufacturing, especially for the automotive and 3C products industries. The developing trends of these related industries can be recognized by forecasting the demand for magnesium alloys. This study develops grey prediction power models to forecast the demand for such a promising green metal material. Grey prediction is an appropriate technique because available data regarding the demand for magnesium alloys are not in line with any statistical assumptions. In particular, because outliers might cause a deterioration of forecasting performance, a robust nonlinear interval regression analysis with neural networks is applied to detect outliers by estimating data intervals. Then, a power model is applied to the newly generated non-equidistant data sequence without outliers. Residual modification is further considered here to improve the forecasting performance of the power model. The forecasting abilities of the proposed grey residual modification models are verified using actual magnesium alloy demand data. The experimental results for ex-post testing show that the mean absolute percentage errors of the proposed models that can work on non-equidistant data were minimal among all considered models.

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Section: Discussionmentioning

confidence: 99%

Demand forecasting of green metal materials using non-equidistant grey prediction with robust nonlinear interval regression analysis

2021

Environ Dev Sustain

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“…In this study, the sequence composed of the values of an index parameter ( β ) that denoting the ε p versus N curve pattern was designated as the feature sequence Y 1 , where β values of 1.0, 2.0, and 3.0 were allotted to the stable, critical, and failure patterns, respectively, according to the recommendations of Javed et al [ 65 ], Wu et al [ 66 ], and Liu et al [ 67 ]. In addition, the data sequences composed of moisture content w , confining pressure σ 3 , and dynamic deviator stress σ d are termed as the observation sequences X 1 , X 2 , and X 3 , respectively.…”

Section: Evaluation Of Critical Dynamic Stress and Final Accumulative Plastic Strainmentioning

confidence: 99%

Evaluation of Critical Dynamic Stress and Accumulative Plastic Strain of an Unbound Granular Material Based on Cyclic Triaxial Tests

et al. 2021

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Critical dynamic stress (σcri) and accumulative plastic strain (εp) are primary indicators regarding the dynamic stability of unbound granular materials (UGMs). This study aims to seek an effective method to evaluate the dynamic stability of UGMs used in railway subgrades. First, the dynamic characteristics of an UGM used in railway subgrade bed construction were investigated by performing a series of large-scale cyclic triaxial tests, with the results showing that εp versus cycle number (N) curves can be categorized into stable, failure, and critical patterns. Grey relational analyses were then established, where the analyzed results demonstrated that the εp–N curve pattern and final accumulative plastic strain (εs) of the stable curves are strongly correlated with the moisture content (w), confining pressure (σ3), and dynamic deviator stress (σd). The analyzed grey relational grades distributed in a narrow range of 0.72 to 0.81, indicating that w, σ3, and σd have similar degrees of importance on determining the εp–N curve patterns and the values of εs of the UGM. Finally, a data processing method using a back-propagation (BP) neural network is introduced to analyze the test data, and an empirical approach is developed to evaluate the σcri (considering the effects of σ3 and w) and εs (considering the effects of σ3, w, and σd) of the UGM. The analyzed results illustrated that the developed method can effectively reflect the linear/non-linear relationships of σcri and εs with respect to σ3 and/or σd. The σcri approximately increases linearly with increasing σ3, and a simple empirical formula is proposed for the σcri. In addition, εs and its variation rate increase non-linearly with increasing σd but decrease non-linearly as σ3 increases.

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“…Ma and Lui [17] proposed a time-delayed polynomial grey prediction model called TDPGM (1,1), a nonhomogeneous grey forecasting model NGM (1,1,k,c) and its optimized (NGMO (1,1,k,c)) is proposed by [4], the grey polynomial model with a tuned background coefficient is proposed by [29], Cui et al [5] developed a parameter optimization method to improve the ONGM (1,1,k) model, Bilgil [3] proposed an exponential grey model named EXGM(1,1), Ma et al [18] developed a novel nonlinear multivariate forecasting grey model based on the Bernoulli equation named NGBMC (1, n), Wang et al. [28] introduced a seasonal grey model called SGM (1,1), Wu et al [33] proposed a new grey model called BNGM (1,1,t 2 ) model, Liu and Wu [15] proposed ANDGM model, kernel-based KARGM(1,1) model is proposed by [16], Li et al [13] developed structureadaptive intelligent grey forecasting model, Wu et al [34] developed a novel grey Riccati model (GRM), the modified grey prediction model with damping trend factor is proposed by [14], the nonlinear grey Bernoulli model with improved parameters, INGBM (1,1), is proposed by [11].…”

Section: Introductionmentioning

confidence: 99%

An optimized continuous fractional grey model for forecasting of the time dependent real world cases

Öztürk

Bilgil

Erdinç

2022

Hacettepe Journal of Mathematics and Statistics

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The new priority in the grey modelling is to build new models that have more accurate forecasting power than the previous ones. This paper aims to develop the prediction performance of the existing continuous grey models. Therefore, a novel continuous grey model (OCCFGM(1,1)) is proposed with conformable fractional derivative. The numerical results of three case studies show that the novel model's prediction accuracy is higher than other competitive models, and the proposed model is more reasonable for practical cases.

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A new grey prediction model and its application to predicting landslide displacement

Cited by 59 publications

References 34 publications

Demand forecasting of green metal materials using non-equidistant grey prediction with robust nonlinear interval regression analysis

Demand forecasting of green metal materials using non-equidistant grey prediction with robust nonlinear interval regression analysis

Evaluation of Critical Dynamic Stress and Accumulative Plastic Strain of an Unbound Granular Material Based on Cyclic Triaxial Tests

An optimized continuous fractional grey model for forecasting of the time dependent real world cases

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